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stat-api — Sports Data

games_on_date

Games scheduled/played on one calendar date (US-Eastern) for nba, mlb, or nhl; defaults to today. Returns the same envelope as query_table on the games table. NFL is week-based, not date-based — for nfl, use query_table on nfl/games with season_id + week filters instead. Requires an API key; rows count against quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoYYYY-MM-DD; defaults to today in US-Eastern time
leagueYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it discloses timezone handling, default-to-today behavior, response envelope reference (same as query_table), API key requirement, and quota consumption. It doesn't explicitly label the operation as read-only, but 'scheduled/played' and quota context imply a read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the primary purpose, followed by return format, then the key exclusion/alternative, and finally operational constraints. Every sentence adds necessary information without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all essential context for correct use: league scope, date semantics, timezone, default, NFL alternative, API key, and quota. Although there is no output schema, referencing the query_table envelope gives a clear pointer to the return format. No significant gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers date with pattern and default, but description adds US-Eastern timezone meaning and clarifies the league enum names explicitly ('nba, mlb, or nhl'). This adds value beyond the schema, especially for league which lacks a description in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns games for a specific calendar date across three leagues (nba, mlb, nhl) with a default to today. It distinguishes itself from siblings by explicitly noting the NFL exception and pointing to query_table as the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use context (date-based leagues) and when-not-to-use (NFL is week-based, use query_table instead). Also mentions default behavior, timezone, API key requirement, and quota impact, giving clear operational guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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